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20232026
most citedHypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design

2 citations · 2 across the 5 of their papers we have counts for

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cs.LG2026

Look Before You Lift: Visual and Quantitative Diagnostics for Topological Deep Learning

Mathilde Papillon, Guillermo Bernárdez, Álvaro Ballón Barreiro +4

Topological deep learning (TDL) methods rely on lifting raw data into higher-order discrete domains such as simplicial complexes, cell complexes, and hypergraphs. In practice, this…

cs.LG2026

OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data

Louisa Cornelis, Johan Mathe, Louis Van Langendonck +2

Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning. Yet most benchmarks focus on the regime , where the number of graphs $n…